{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "pdf-title"
    ]
   },
   "source": [
    "# Multiclass Support Vector Machine exercise\n",
    "\n",
    "*Complete and hand in this completed worksheet (including its outputs and any supporting code outside of the worksheet) with your assignment submission. For more details see the [assignments page](http://vision.stanford.edu/teaching/cs231n/assignments.html) on the course website.*\n",
    "\n",
    "In this exercise you will:\n",
    "    \n",
    "- implement a fully-vectorized **loss function** for the SVM\n",
    "- implement the fully-vectorized expression for its **analytic gradient**\n",
    "- **check your implementation** using numerical gradient\n",
    "- use a validation set to **tune the learning rate and regularization** strength\n",
    "- **optimize** the loss function with **SGD**\n",
    "- **visualize** the final learned weights\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "tags": [
     "pdf-ignore"
    ]
   },
   "outputs": [],
   "source": [
    "# Run some setup code for this notebook.\n",
    "import random\n",
    "import numpy as np\n",
    "from cs231n.data_utils import load_CIFAR10\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# This is a bit of magic to make matplotlib figures appear inline in the\n",
    "# notebook rather than in a new window.\n",
    "%matplotlib inline\n",
    "plt.rcParams['figure.figsize'] = (10.0, 8.0) # set default size of plots\n",
    "plt.rcParams['image.interpolation'] = 'nearest'\n",
    "plt.rcParams['image.cmap'] = 'gray'\n",
    "\n",
    "# Some more magic so that the notebook will reload external python modules;\n",
    "# see http://stackoverflow.com/questions/1907993/autoreload-of-modules-in-ipython\n",
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "pdf-ignore"
    ]
   },
   "source": [
    "## CIFAR-10 Data Loading and Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "tags": [
     "pdf-ignore"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training data shape:  (50000, 32, 32, 3)\n",
      "Training labels shape:  (50000,)\n",
      "Test data shape:  (10000, 32, 32, 3)\n",
      "Test labels shape:  (10000,)\n"
     ]
    }
   ],
   "source": [
    "# Load the raw CIFAR-10 data.\n",
    "cifar10_dir = 'cs231n/datasets/cifar-10-batches-py'\n",
    "\n",
    "# Cleaning up variables to prevent loading data multiple times (which may cause memory issue)\n",
    "try:\n",
    "   del X_train, y_train\n",
    "   del X_test, y_test\n",
    "   print('Clear previously loaded data.')\n",
    "except:\n",
    "   pass\n",
    "\n",
    "X_train, y_train, X_test, y_test = load_CIFAR10(cifar10_dir)\n",
    "\n",
    "# As a sanity check, we print out the size of the training and test data.\n",
    "print('Training data shape: ', X_train.shape)\n",
    "print('Training labels shape: ', y_train.shape)\n",
    "print('Test data shape: ', X_test.shape)\n",
    "print('Test labels shape: ', y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "tags": [
     "pdf-ignore"
    ]
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 70 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize some examples from the dataset.\n",
    "# We show a few examples of training images from each class.\n",
    "classes = ['plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']\n",
    "num_classes = len(classes)\n",
    "samples_per_class = 7\n",
    "for y, cls in enumerate(classes):\n",
    "    idxs = np.flatnonzero(y_train == y)\n",
    "    idxs = np.random.choice(idxs, samples_per_class, replace=False)\n",
    "    for i, idx in enumerate(idxs):\n",
    "        plt_idx = i * num_classes + y + 1\n",
    "        plt.subplot(samples_per_class, num_classes, plt_idx)\n",
    "        plt.imshow(X_train[idx].astype('uint8'))\n",
    "        plt.axis('off')\n",
    "        if i == 0:\n",
    "            plt.title(cls)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "tags": [
     "pdf-ignore"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train data shape:  (49000, 32, 32, 3)\n",
      "Train labels shape:  (49000,)\n",
      "Validation data shape:  (1000, 32, 32, 3)\n",
      "Validation labels shape:  (1000,)\n",
      "Test data shape:  (1000, 32, 32, 3)\n",
      "Test labels shape:  (1000,)\n"
     ]
    }
   ],
   "source": [
    "# Split the data into train, val, and test sets. In addition we will\n",
    "# create a small development set as a subset of the training data;\n",
    "# we can use this for development so our code runs faster.\n",
    "num_training = 49000\n",
    "num_validation = 1000\n",
    "num_test = 1000\n",
    "num_dev = 500\n",
    "\n",
    "# Our validation set will be num_validation points from the original\n",
    "# training set.\n",
    "mask = range(num_training, num_training + num_validation)\n",
    "X_val = X_train[mask]\n",
    "y_val = y_train[mask]\n",
    "\n",
    "# Our training set will be the first num_train points from the original\n",
    "# training set.\n",
    "mask = range(num_training)\n",
    "X_train = X_train[mask]\n",
    "y_train = y_train[mask]\n",
    "\n",
    "# We will also make a development set, which is a small subset of\n",
    "# the training set.\n",
    "mask = np.random.choice(num_training, num_dev, replace=False)\n",
    "X_dev = X_train[mask]\n",
    "y_dev = y_train[mask]\n",
    "\n",
    "# We use the first num_test points of the original test set as our\n",
    "# test set.\n",
    "mask = range(num_test)\n",
    "X_test = X_test[mask]\n",
    "y_test = y_test[mask]\n",
    "\n",
    "print('Train data shape: ', X_train.shape)\n",
    "print('Train labels shape: ', y_train.shape)\n",
    "print('Validation data shape: ', X_val.shape)\n",
    "print('Validation labels shape: ', y_val.shape)\n",
    "print('Test data shape: ', X_test.shape)\n",
    "print('Test labels shape: ', y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "tags": [
     "pdf-ignore"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training data shape:  (49000, 3072)\n",
      "Validation data shape:  (1000, 3072)\n",
      "Test data shape:  (1000, 3072)\n",
      "dev data shape:  (500, 3072)\n"
     ]
    }
   ],
   "source": [
    "# Preprocessing: reshape the image data into rows\n",
    "X_train = np.reshape(X_train, (X_train.shape[0], -1))\n",
    "X_val = np.reshape(X_val, (X_val.shape[0], -1))\n",
    "X_test = np.reshape(X_test, (X_test.shape[0], -1))\n",
    "X_dev = np.reshape(X_dev, (X_dev.shape[0], -1))\n",
    "\n",
    "# As a sanity check, print out the shapes of the data\n",
    "print('Training data shape: ', X_train.shape)\n",
    "print('Validation data shape: ', X_val.shape)\n",
    "print('Test data shape: ', X_test.shape)\n",
    "print('dev data shape: ', X_dev.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "tags": [
     "pdf-ignore-input"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[130.64189796 135.98173469 132.47391837 130.05569388 135.34804082\n",
      " 131.75402041 130.96055102 136.14328571 132.47636735 131.48467347]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(49000, 3073) (1000, 3073) (1000, 3073) (500, 3073)\n"
     ]
    }
   ],
   "source": [
    "# Preprocessing: subtract the mean image\n",
    "# first: compute the image mean based on the training data\n",
    "mean_image = np.mean(X_train, axis=0)\n",
    "print(mean_image[:10]) # print a few of the elements\n",
    "plt.figure(figsize=(4,4))\n",
    "plt.imshow(mean_image.reshape((32,32,3)).astype('uint8')) # visualize the mean image\n",
    "plt.show()\n",
    "\n",
    "# second: subtract the mean image from train and test data\n",
    "X_train -= mean_image\n",
    "X_val -= mean_image\n",
    "X_test -= mean_image\n",
    "X_dev -= mean_image\n",
    "\n",
    "# third: append the bias dimension of ones (i.e. bias trick) so that our SVM\n",
    "# only has to worry about optimizing a single weight matrix W.\n",
    "X_train = np.hstack([X_train, np.ones((X_train.shape[0], 1))])\n",
    "X_val = np.hstack([X_val, np.ones((X_val.shape[0], 1))])\n",
    "X_test = np.hstack([X_test, np.ones((X_test.shape[0], 1))])\n",
    "X_dev = np.hstack([X_dev, np.ones((X_dev.shape[0], 1))])\n",
    "\n",
    "print(X_train.shape, X_val.shape, X_test.shape, X_dev.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## SVM Classifier\n",
    "\n",
    "Your code for this section will all be written inside **cs231n/classifiers/linear_svm.py**. \n",
    "\n",
    "As you can see, we have prefilled the function `compute_loss_naive` which uses for loops to evaluate the multiclass SVM loss function. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loss: 8.372943\n"
     ]
    }
   ],
   "source": [
    "# Evaluate the naive implementation of the loss we provided for you:\n",
    "from cs231n.classifiers.linear_svm import svm_loss_naive\n",
    "import time\n",
    "\n",
    "# generate a random SVM weight matrix of small numbers\n",
    "W = np.random.randn(3073, 10) * 0.0001 \n",
    "\n",
    "loss, grad = svm_loss_naive(W, X_dev, y_dev, 0.000005)\n",
    "print('loss: %f' % (loss, ))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `grad` returned from the function above is right now all zero. Derive and implement the gradient for the SVM cost function and implement it inline inside the function `svm_loss_naive`. You will find it helpful to interleave your new code inside the existing function.\n",
    "\n",
    "To check that you have correctly implemented the gradient correctly, you can numerically estimate the gradient of the loss function and compare the numeric estimate to the gradient that you computed. We have provided code that does this for you:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "numerical: 6.903506 analytic: 6.903506, relative error: 5.617043e-11\n",
      "numerical: 17.666973 analytic: 17.666973, relative error: 6.685253e-12\n",
      "numerical: 3.133986 analytic: 3.133986, relative error: 1.867131e-11\n",
      "numerical: 3.592639 analytic: 3.592639, relative error: 9.470404e-11\n",
      "numerical: 8.953840 analytic: 8.953840, relative error: 4.879246e-11\n",
      "numerical: -47.164065 analytic: -47.164065, relative error: 3.604383e-12\n",
      "numerical: -43.001646 analytic: -43.001646, relative error: 1.749273e-12\n",
      "numerical: 12.853902 analytic: 12.853902, relative error: 3.988403e-12\n",
      "numerical: -0.036000 analytic: -0.036000, relative error: 9.297077e-09\n",
      "numerical: 21.158825 analytic: 21.158825, relative error: 1.116716e-11\n",
      "numerical: -20.954596 analytic: -20.954596, relative error: 1.014455e-11\n",
      "numerical: -30.853186 analytic: -30.853186, relative error: 1.075607e-11\n",
      "numerical: -11.981873 analytic: -11.981873, relative error: 4.616768e-12\n",
      "numerical: 5.719640 analytic: 5.719640, relative error: 6.728298e-11\n",
      "numerical: -2.763430 analytic: -2.763430, relative error: 5.598977e-11\n",
      "numerical: 11.025129 analytic: 11.025129, relative error: 8.816346e-12\n",
      "numerical: 18.499824 analytic: 18.499824, relative error: 7.473540e-12\n",
      "numerical: 1.755659 analytic: 1.755659, relative error: 3.578067e-12\n",
      "numerical: 4.155787 analytic: 4.155787, relative error: 4.251987e-11\n",
      "numerical: -12.638760 analytic: -12.638760, relative error: 1.099327e-11\n"
     ]
    }
   ],
   "source": [
    "# Once you've implemented the gradient, recompute it with the code below\n",
    "# and gradient check it with the function we provided for you\n",
    "\n",
    "# Compute the loss and its gradient at W.\n",
    "loss, grad = svm_loss_naive(W, X_dev, y_dev, 0.0)\n",
    "\n",
    "# Numerically compute the gradient along several randomly chosen dimensions, and\n",
    "# compare them with your analytically computed gradient. The numbers should match\n",
    "# almost exactly along all dimensions.\n",
    "from cs231n.gradient_check import grad_check_sparse\n",
    "f = lambda w: svm_loss_naive(w, X_dev, y_dev, 0.0)[0]\n",
    "grad_numerical = grad_check_sparse(f, W, grad)\n",
    "\n",
    "# do the gradient check once again with regularization turned on\n",
    "# you didn't forget the regularization gradient did you?\n",
    "loss, grad = svm_loss_naive(W, X_dev, y_dev, 5e1)\n",
    "f = lambda w: svm_loss_naive(w, X_dev, y_dev, 5e1)[0]\n",
    "grad_numerical = grad_check_sparse(f, W, grad)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "pdf-inline"
    ]
   },
   "source": [
    "**Inline Question 1**\n",
    "\n",
    "It is possible that once in a while a dimension in the gradcheck will not match exactly. What could such a discrepancy be caused by? Is it a reason for concern? What is a simple example in one dimension where a gradient check could fail? How would change the margin affect of the frequency of this happening? *Hint: the SVM loss function is not strictly speaking differentiable*\n",
    "\n",
    "$\\color{blue}{\\textit Your Answer:}$ *fill this in.*  \n",
    "In some points SVM loss function can not differentiable,like ReLU in the zero point it can't differente."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Naive loss: 8.372943e+00 computed in 0.248857s\n",
      "Vectorized loss: 8.372943e+00 computed in 0.026984s\n",
      "difference: -0.000000\n"
     ]
    }
   ],
   "source": [
    "# Next implement the function svm_loss_vectorized; for now only compute the loss;\n",
    "# we will implement the gradient in a moment.\n",
    "tic = time.time()\n",
    "loss_naive, grad_naive = svm_loss_naive(W, X_dev, y_dev, 0.000005)\n",
    "toc = time.time()\n",
    "print('Naive loss: %e computed in %fs' % (loss_naive, toc - tic))\n",
    "\n",
    "from cs231n.classifiers.linear_svm import svm_loss_vectorized\n",
    "tic = time.time()\n",
    "loss_vectorized, _ = svm_loss_vectorized(W, X_dev, y_dev, 0.000005)\n",
    "toc = time.time()\n",
    "print('Vectorized loss: %e computed in %fs' % (loss_vectorized, toc - tic))\n",
    "\n",
    "# The losses should match but your vectorized implementation should be much faster.\n",
    "print('difference: %f' % (loss_naive - loss_vectorized))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Naive loss and gradient: computed in 0.208877s\n",
      "Vectorized loss and gradient: computed in 0.027984s\n",
      "difference: 0.000000\n"
     ]
    }
   ],
   "source": [
    "# Complete the implementation of svm_loss_vectorized, and compute the gradient\n",
    "# of the loss function in a vectorized way.\n",
    "\n",
    "# The naive implementation and the vectorized implementation should match, but\n",
    "# the vectorized version should still be much faster.\n",
    "tic = time.time()\n",
    "_, grad_naive = svm_loss_naive(W, X_dev, y_dev, 0.000005)\n",
    "toc = time.time()\n",
    "print('Naive loss and gradient: computed in %fs' % (toc - tic))\n",
    "\n",
    "tic = time.time()\n",
    "_, grad_vectorized = svm_loss_vectorized(W, X_dev, y_dev, 0.000005)\n",
    "toc = time.time()\n",
    "print('Vectorized loss and gradient: computed in %fs' % (toc - tic))\n",
    "\n",
    "# The loss is a single number, so it is easy to compare the values computed\n",
    "# by the two implementations. The gradient on the other hand is a matrix, so\n",
    "# we use the Frobenius norm to compare them.\n",
    "difference = np.linalg.norm(grad_naive - grad_vectorized, ord='fro')\n",
    "print('difference: %f' % difference)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Stochastic Gradient Descent\n",
    "\n",
    "We now have vectorized and efficient expressions for the loss, the gradient and our gradient matches the numerical gradient. We are therefore ready to do SGD to minimize the loss."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iteration 0 / 1500: loss 786.933344\n",
      "iteration 100 / 1500: loss 469.131045\n",
      "iteration 200 / 1500: loss 284.882024\n",
      "iteration 300 / 1500: loss 173.207780\n",
      "iteration 400 / 1500: loss 106.739048\n",
      "iteration 500 / 1500: loss 65.944691\n",
      "iteration 600 / 1500: loss 42.134019\n",
      "iteration 700 / 1500: loss 27.587262\n",
      "iteration 800 / 1500: loss 18.602609\n",
      "iteration 900 / 1500: loss 13.208623\n",
      "iteration 1000 / 1500: loss 10.055504\n",
      "iteration 1100 / 1500: loss 8.270745\n",
      "iteration 1200 / 1500: loss 7.666906\n",
      "iteration 1300 / 1500: loss 7.232520\n",
      "iteration 1400 / 1500: loss 6.238883\n",
      "That took 57.224993s\n"
     ]
    }
   ],
   "source": [
    "# In the file linear_classifier.py, implement SGD in the function\n",
    "# LinearClassifier.train() and then run it with the code below.\n",
    "from cs231n.classifiers import LinearSVM\n",
    "svm = LinearSVM()\n",
    "tic = time.time()\n",
    "loss_hist = svm.train(X_train, y_train, learning_rate=1e-7, reg=2.5e4,\n",
    "                      num_iters=1500, verbose=True)\n",
    "toc = time.time()\n",
    "print('That took %fs' % (toc - tic))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# A useful debugging strategy is to plot the loss as a function of\n",
    "# iteration number:\n",
    "plt.plot(loss_hist)\n",
    "plt.xlabel('Iteration number')\n",
    "plt.ylabel('Loss value')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "training accuracy: 0.375857\n",
      "validation accuracy: 0.387000\n"
     ]
    }
   ],
   "source": [
    "# Write the LinearSVM.predict function and evaluate the performance on both the\n",
    "# training and validation set\n",
    "y_train_pred = svm.predict(X_train)\n",
    "print('training accuracy: %f' % (np.mean(y_train == y_train_pred), ))\n",
    "y_val_pred = svm.predict(X_val)\n",
    "print('validation accuracy: %f' % (np.mean(y_val == y_val_pred), ))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "tags": [
     "code"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iteration 0 / 1500: loss 798.525173\n",
      "iteration 100 / 1500: loss 475.160487\n",
      "iteration 200 / 1500: loss 288.481936\n",
      "iteration 300 / 1500: loss 176.921789\n",
      "iteration 400 / 1500: loss 107.695612\n",
      "iteration 500 / 1500: loss 67.032467\n",
      "iteration 600 / 1500: loss 43.177347\n",
      "iteration 700 / 1500: loss 27.170587\n",
      "iteration 800 / 1500: loss 18.938465\n",
      "iteration 900 / 1500: loss 13.524029\n",
      "iteration 1000 / 1500: loss 10.334331\n",
      "iteration 1100 / 1500: loss 8.137584\n",
      "iteration 1200 / 1500: loss 7.206025\n",
      "iteration 1300 / 1500: loss 6.105129\n",
      "iteration 1400 / 1500: loss 5.991950\n",
      "iteration 0 / 1500: loss 1557.806454\n",
      "iteration 100 / 1500: loss 566.973629\n",
      "iteration 200 / 1500: loss 210.188307\n",
      "iteration 300 / 1500: loss 80.069938\n",
      "iteration 400 / 1500: loss 32.943006\n",
      "iteration 500 / 1500: loss 15.415323\n",
      "iteration 600 / 1500: loss 9.266044\n",
      "iteration 700 / 1500: loss 7.367397\n",
      "iteration 800 / 1500: loss 6.032296\n",
      "iteration 900 / 1500: loss 5.801545\n",
      "iteration 1000 / 1500: loss 6.269460\n",
      "iteration 1100 / 1500: loss 5.475871\n",
      "iteration 1200 / 1500: loss 5.581522\n",
      "iteration 1300 / 1500: loss 5.746696\n",
      "iteration 1400 / 1500: loss 5.675167\n",
      "iteration 0 / 1500: loss 788.767836\n",
      "iteration 100 / 1500: loss 2050.717376\n",
      "iteration 200 / 1500: loss 1990.866632\n",
      "iteration 300 / 1500: loss 1608.187657\n",
      "iteration 400 / 1500: loss 1127.098568\n",
      "iteration 500 / 1500: loss 1432.134882\n",
      "iteration 600 / 1500: loss 1518.967593\n",
      "iteration 700 / 1500: loss 1280.556997\n",
      "iteration 800 / 1500: loss 1220.855024\n",
      "iteration 900 / 1500: loss 1270.353120\n",
      "iteration 1000 / 1500: loss 1481.347369\n",
      "iteration 1100 / 1500: loss 1509.283572\n",
      "iteration 1200 / 1500: loss 1391.225407\n",
      "iteration 1300 / 1500: loss 1154.441104\n",
      "iteration 1400 / 1500: loss 1300.203701\n",
      "iteration 0 / 1500: loss 1539.318966\n",
      "iteration 100 / 1500: loss 781869119100372923162957600033133821952.000000\n",
      "iteration 200 / 1500: loss 129236703459146384650201062677214897307646450777523253797463611406907604992.000000\n",
      "iteration 300 / 1500: loss 21361792035225777085383627794994227337792319317536241952798087014636510859903128097889056835299913092279828480.000000\n",
      "iteration 400 / 1500: loss 3530933138514221113743314396864967104784399328406368480670598211127704642900090486237998959686112027574200947573962348092211327418986940153724928.000000\n",
      "iteration 500 / 1500: loss 583634968831214642120474190889573522046845008854107636426640564058590363029045658779062907077629498206774486000860522642961696643341721983372987594968564071347179123687844378312704.000000\n",
      "iteration 600 / 1500: loss 96470186061338538026893356307043511169358496596183866727782399758754425016533607469319662930144819701453351602263709720027518051403947724715913998597760553264591230544846890774510769948533862235169717909010436325376.000000\n",
      "iteration 700 / 1500: loss 15945749133823204918986051109577136723116101562727591541145095572276843321937024179694859279738853523478090367147294811120750917304344488290110724677135626715052866637601170093581924601749184471161334272987320423309457706482494411143132468134790823936.000000\n",
      "iteration 800 / 1500: loss 2635704623573061888745842321216094824160978153578938605353966158213450616139955377086066882661736023515476707139144051591997550308082654222848395999313669530933483592927750201781244752536666239902544643372823926070736791664773734099148081059144199694541361884421698286700813699979935744.000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Codes\\PycharmProjects\\CS231nAssignment\\assignment1\\cs231n\\classifiers\\linear_svm.py:87: RuntimeWarning: overflow encountered in double_scalars\n",
      "  loss += reg * np.sum(W * W)\n",
      "d:\\programs\\anaconda\\envs\\cs231na1\\lib\\site-packages\\numpy\\core\\fromnumeric.py:86: RuntimeWarning: overflow encountered in reduce\n",
      "  return ufunc.reduce(obj, axis, dtype, out, **passkwargs)\n",
      "D:\\Codes\\PycharmProjects\\CS231nAssignment\\assignment1\\cs231n\\classifiers\\linear_svm.py:87: RuntimeWarning: overflow encountered in multiply\n",
      "  loss += reg * np.sum(W * W)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iteration 900 / 1500: loss inf\n",
      "iteration 1000 / 1500: loss inf\n",
      "iteration 1100 / 1500: loss inf\n",
      "iteration 1200 / 1500: loss inf\n",
      "iteration 1300 / 1500: loss inf\n",
      "iteration 1400 / 1500: loss inf\n",
      "lr 1.000000e-07 reg 2.500000e+04 train accuracy: 0.382980 val accuracy: 0.395000\n",
      "lr 1.000000e-07 reg 5.000000e+04 train accuracy: 0.370163 val accuracy: 0.377000\n",
      "lr 5.000000e-05 reg 2.500000e+04 train accuracy: 0.168306 val accuracy: 0.164000\n",
      "lr 5.000000e-05 reg 5.000000e+04 train accuracy: 0.137490 val accuracy: 0.144000\n",
      "best validation accuracy achieved during cross-validation: 0.395000\n"
     ]
    }
   ],
   "source": [
    "# Use the validation set to tune hyperparameters (regularization strength and\n",
    "# learning rate). You should experiment with different ranges for the learning\n",
    "# rates and regularization strengths; if you are careful you should be able to\n",
    "# get a classification accuracy of about 0.39 on the validation set.\n",
    "\n",
    "#Note: you may see runtime/overflow warnings during hyper-parameter search. \n",
    "# This may be caused by extreme values, and is not a bug.\n",
    "\n",
    "learning_rates = [1e-7, 5e-5]\n",
    "regularization_strengths = [2.5e4, 5e4]\n",
    "\n",
    "# results is dictionary mapping tuples of the form\n",
    "# (learning_rate, regularization_strength) to tuples of the form\n",
    "# (training_accuracy, validation_accuracy). The accuracy is simply the fraction\n",
    "# of data points that are correctly classified.\n",
    "results = {}\n",
    "best_val = -1   # The highest validation accuracy that we have seen so far.\n",
    "best_svm = None # The LinearSVM object that achieved the highest validation rate.\n",
    "\n",
    "################################################################################\n",
    "# TODO:                                                                        #\n",
    "# Write code that chooses the best hyperparameters by tuning on the validation #\n",
    "# set. For each combination of hyperparameters, train a linear SVM on the      #\n",
    "# training set, compute its accuracy on the training and validation sets, and  #\n",
    "# store these numbers in the results dictionary. In addition, store the best   #\n",
    "# validation accuracy in best_val and the LinearSVM object that achieves this  #\n",
    "# accuracy in best_svm.                                                        #\n",
    "#                                                                              #\n",
    "# Hint: You should use a small value for num_iters as you develop your         #\n",
    "# validation code so that the SVMs don't take much time to train; once you are #\n",
    "# confident that your validation code works, you should rerun the validation   #\n",
    "# code with a larger value for num_iters.                                      #\n",
    "################################################################################\n",
    "# *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n",
    "\n",
    "for rate in learning_rates:\n",
    "    for reg in regularization_strengths:\n",
    "        svm=LinearSVM()\n",
    "        svm.train(X_train,y_train,learning_rate=rate,reg=reg,num_iters=1500,verbose=True)\n",
    "        y_train_pred = svm.predict(X_train)\n",
    "        y_val_pred = svm.predict(X_val)\n",
    "        val=np.mean(y_val == y_val_pred)\n",
    "        if val>best_val:\n",
    "            best_svm=svm\n",
    "            best_val=val\n",
    "        results[(rate,reg)]=(np.mean(y_train == y_train_pred),val)\n",
    "        # loss_hist = svm.train(X_train, y_train, learning_rate=1e-7, reg=2.5e4,num_iters=1500, verbose=True)\n",
    "\n",
    "# *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n",
    "    \n",
    "# Print out results.\n",
    "for lr, reg in sorted(results):\n",
    "    train_accuracy, val_accuracy = results[(lr, reg)]\n",
    "    print('lr %e reg %e train accuracy: %f val accuracy: %f' % (\n",
    "                lr, reg, train_accuracy, val_accuracy))\n",
    "    \n",
    "print('best validation accuracy achieved during cross-validation: %f' % best_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "tags": [
     "pdf-ignore-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize the cross-validation results\n",
    "import math\n",
    "x_scatter = [math.log10(x[0]) for x in results]\n",
    "y_scatter = [math.log10(x[1]) for x in results]\n",
    "\n",
    "# plot training accuracy\n",
    "marker_size = 100\n",
    "colors = [results[x][0] for x in results]\n",
    "plt.subplot(2, 1, 1)\n",
    "plt.scatter(x_scatter, y_scatter, marker_size, c=colors, cmap=plt.cm.coolwarm)\n",
    "plt.colorbar()\n",
    "plt.xlabel('log learning rate')\n",
    "plt.ylabel('log regularization strength')\n",
    "plt.title('CIFAR-10 training accuracy')\n",
    "\n",
    "# plot validation accuracy\n",
    "colors = [results[x][1] for x in results] # default size of markers is 20\n",
    "plt.subplot(2, 1, 2)\n",
    "plt.scatter(x_scatter, y_scatter, marker_size, c=colors, cmap=plt.cm.coolwarm)\n",
    "plt.colorbar()\n",
    "plt.xlabel('log learning rate')\n",
    "plt.ylabel('log regularization strength')\n",
    "plt.title('CIFAR-10 validation accuracy')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "linear SVM on raw pixels final test set accuracy: 0.378000\n"
     ]
    }
   ],
   "source": [
    "# Evaluate the best svm on test set\n",
    "y_test_pred = best_svm.predict(X_test)\n",
    "test_accuracy = np.mean(y_test == y_test_pred)\n",
    "print('linear SVM on raw pixels final test set accuracy: %f' % test_accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "tags": [
     "pdf-ignore-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 10 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize the learned weights for each class.\n",
    "# Depending on your choice of learning rate and regularization strength, these may\n",
    "# or may not be nice to look at.\n",
    "w = best_svm.W[:-1,:] # strip out the bias\n",
    "w = w.reshape(32, 32, 3, 10)\n",
    "w_min, w_max = np.min(w), np.max(w)\n",
    "classes = ['plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']\n",
    "for i in range(10):\n",
    "    plt.subplot(2, 5, i + 1)\n",
    "      \n",
    "    # Rescale the weights to be between 0 and 255\n",
    "    wimg = 255.0 * (w[:, :, :, i].squeeze() - w_min) / (w_max - w_min)\n",
    "    plt.imshow(wimg.astype('uint8'))\n",
    "    plt.axis('off')\n",
    "    plt.title(classes[i])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "pdf-inline"
    ]
   },
   "source": [
    "**Inline question 2**\n",
    "\n",
    "Describe what your visualized SVM weights look like, and offer a brief explanation for why they look they way that they do.\n",
    "\n",
    "$\\color{blue}{\\textit Your Answer:}$ *fill this in*  \n",
    "说实在话我看不出什么。。。"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
